What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Radiology
His main research concerns Artificial intelligence, Computer vision, Segmentation, Radiology and Pattern recognition.
When carried out as part of a general Artificial intelligence research project, his work on Orientation is frequently linked to work in Branching points, therefore connecting diverse disciplines of study.
His work carried out in the field of Computer vision brings together such families of science as Lung volumes, Consistency and Pattern recognition.
He has researched Segmentation in several fields, including Image processing, Tree, Atrial fibrillation and Vertebra.
In Radiology, Cristian Lorenz works on issues like Breathing, which are connected to Nuclear medicine, Lung, Single-photon emission computed tomography and Hounsfield scale.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Second derivative, Bronchus and Vein.
His most cited work include:
- Automatic Model-Based Segmentation of the Heart in CT Images (355 citations)
- Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge (330 citations)
- Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images (267 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Image.
Artificial intelligence and Set are two areas of study in which he engages in interdisciplinary research.
His Computer vision study combines topics from a wide range of disciplines, such as Surface and Position.
He combines subjects such as Vertebra and Medical imaging with his study of Segmentation.
His study looks at the relationship between Pattern recognition and fields such as Tree, as well as how they intersect with chemical problems.
His research investigates the connection between Hough transform and topics such as Discriminative model that intersect with issues in Point.
He most often published in these fields:
- Artificial intelligence (70.77%)
- Computer vision (58.97%)
- Segmentation (30.77%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial intelligence (70.77%)
- Segmentation (30.77%)
- Pattern recognition (22.05%)
In recent papers he was focusing on the following fields of study:
Cristian Lorenz focuses on Artificial intelligence, Segmentation, Pattern recognition, Computer vision and Ground truth.
His Artificial intelligence study often links to related topics such as Torso.
His research in Segmentation intersects with topics in Artery, Internal medicine, Feature and 3D ultrasound, Ultrasound.
His studies deal with areas such as Decision tree, Centroid and Pairwise comparison as well as Pattern recognition.
His studies in Computer vision integrate themes in fields like Artificial neural network, Supine position and Surface.
His research investigates the link between Ground truth and topics such as Imaging phantom that cross with problems in Similarity, Hausdorff distance and Real image.
Between 2015 and 2020, his most popular works were:
- Four challenges in medical image analysis from an industrial perspective. (39 citations)
- Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. (21 citations)
- Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Radiology
Cristian Lorenz spends much of his time researching Artificial intelligence, Segmentation, Computer vision, Bone mineral and Osteoporosis.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
Cristian Lorenz has included themes like Artery, Heart tissues, Internal medicine, Cardiology and Image based in his Segmentation study.
Many of his research projects under Computer vision are closely connected to Mammography with Mammography, tying the diverse disciplines of science together.
His Bone mineral study which covers Cohort that intersects with Voxel.
His Osteoporosis research is multidisciplinary, relying on both Nuclear medicine, Incidence, Densitometry and Vertebra.
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